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Development and Validation of an Empirical Ocean Color Algorithm with Uncertainties: A Case Study with the Particulate Backscattering Coefficient

We explored how algorithm (model) and in situ measurement (observation) uncertainties can effectively be incorporated into empirical ocean color model development and assessment. In this study we focused on methods for deriving the particulate backscattering coefficient at 555 nm, b (bp)(555) (m(−1)...

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Autores principales: McKinna, Lachlan I. W., Cetinić, Ivona, Werdell, P. Jeremy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8244078/
https://www.ncbi.nlm.nih.gov/pubmed/34221787
http://dx.doi.org/10.1029/2021JC017231
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author McKinna, Lachlan I. W.
Cetinić, Ivona
Werdell, P. Jeremy
author_facet McKinna, Lachlan I. W.
Cetinić, Ivona
Werdell, P. Jeremy
author_sort McKinna, Lachlan I. W.
collection PubMed
description We explored how algorithm (model) and in situ measurement (observation) uncertainties can effectively be incorporated into empirical ocean color model development and assessment. In this study we focused on methods for deriving the particulate backscattering coefficient at 555 nm, b (bp)(555) (m(−1)). We developed a simple empirical algorithm for deriving b (bp)(555) as a function of a remote sensing reflectance line height (LH) metric. Model training was performed using a high‐quality bio‐optical dataset that contains coincident in situ measurements of the spectral remote sensing reflectances, R (rs)(λ) (sr(−1)), and the spectral particulate backscattering coefficients, b (bp)(λ). The LH metric used is defined as the magnitude of R (rs)(555) relative to a linear baseline drawn between R (rs)(490) and R (rs)(670). Using an independent validation dataset, we compared the skill of the LH‐based model with two other models. We used contemporary validation metrics, including bias and mean absolute error (MAE), that were corrected for model and observation uncertainties. The results demonstrated that measurement uncertainties do indeed impact contemporary validation metrics such as mean bias and MAE. Zeta‐scores and z‐tests for overlapping confidence intervals were also explored as potential methods for assessing model skill.
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spelling pubmed-82440782021-07-02 Development and Validation of an Empirical Ocean Color Algorithm with Uncertainties: A Case Study with the Particulate Backscattering Coefficient McKinna, Lachlan I. W. Cetinić, Ivona Werdell, P. Jeremy J Geophys Res Oceans Research Article We explored how algorithm (model) and in situ measurement (observation) uncertainties can effectively be incorporated into empirical ocean color model development and assessment. In this study we focused on methods for deriving the particulate backscattering coefficient at 555 nm, b (bp)(555) (m(−1)). We developed a simple empirical algorithm for deriving b (bp)(555) as a function of a remote sensing reflectance line height (LH) metric. Model training was performed using a high‐quality bio‐optical dataset that contains coincident in situ measurements of the spectral remote sensing reflectances, R (rs)(λ) (sr(−1)), and the spectral particulate backscattering coefficients, b (bp)(λ). The LH metric used is defined as the magnitude of R (rs)(555) relative to a linear baseline drawn between R (rs)(490) and R (rs)(670). Using an independent validation dataset, we compared the skill of the LH‐based model with two other models. We used contemporary validation metrics, including bias and mean absolute error (MAE), that were corrected for model and observation uncertainties. The results demonstrated that measurement uncertainties do indeed impact contemporary validation metrics such as mean bias and MAE. Zeta‐scores and z‐tests for overlapping confidence intervals were also explored as potential methods for assessing model skill. John Wiley and Sons Inc. 2021-05-03 2021-05 /pmc/articles/PMC8244078/ /pubmed/34221787 http://dx.doi.org/10.1029/2021JC017231 Text en © 2021. The Authors. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Research Article
McKinna, Lachlan I. W.
Cetinić, Ivona
Werdell, P. Jeremy
Development and Validation of an Empirical Ocean Color Algorithm with Uncertainties: A Case Study with the Particulate Backscattering Coefficient
title Development and Validation of an Empirical Ocean Color Algorithm with Uncertainties: A Case Study with the Particulate Backscattering Coefficient
title_full Development and Validation of an Empirical Ocean Color Algorithm with Uncertainties: A Case Study with the Particulate Backscattering Coefficient
title_fullStr Development and Validation of an Empirical Ocean Color Algorithm with Uncertainties: A Case Study with the Particulate Backscattering Coefficient
title_full_unstemmed Development and Validation of an Empirical Ocean Color Algorithm with Uncertainties: A Case Study with the Particulate Backscattering Coefficient
title_short Development and Validation of an Empirical Ocean Color Algorithm with Uncertainties: A Case Study with the Particulate Backscattering Coefficient
title_sort development and validation of an empirical ocean color algorithm with uncertainties: a case study with the particulate backscattering coefficient
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8244078/
https://www.ncbi.nlm.nih.gov/pubmed/34221787
http://dx.doi.org/10.1029/2021JC017231
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